EEG To FMRI Synthesis: Is Deep Learning a Candidate?

Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task of learning a mapping function between multivariate time series with highly dissimilar structures. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adversarial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance. EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring. The code used in this manuscript is available in Github and the datasets are open source

Exploiting Associations between Class Labels in Multi-label Classification

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases can bring about significant improvements. In this paper, we have introduced positive, negative and hybrid relationships between the class labels for the first time and we have proposed a method to extract these relations for a multi-label classification task and consequently, to use them in order to improve the predictions made by a multi-label classifier. We have conducted extensive experiments to assess the effectiveness of the proposed method. The obtained results advocate the merits of the proposed method in improving the multi-label classification results

Pairwise Learning of Multilabel Classifications with Perceptrons

Multiclass Multilabel Perceptrons (MMP) are an efficient incremental algorithm for training a team of perceptrons for a multilabel prediction task. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative approach that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. An evaluation on the Reuters 2000 (RCV1) data shows that our multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example. 1

Pairwise Learning of Multilabel Classifications with Perceptrons

Multiclass Multilabel Perceptrons (MMP) are an efficient incremental algorithm for training a team of perceptrons for a multilabel prediction task. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative approach that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. An evaluation on the Reuters 2000 (RCV1) data shows that our multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example

Pairwise learning of multilabel classifications with perceptrons

Multiclass multilabel perceptrons (MMP) have been proposed as an efficient incremental training algorithm for addressing a multilabel prediction task with a team of perceptrons. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative technique that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. Our evaluation on four multilabel datasets shows that the multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example

An investigation of multi-label classification techniques for predicting HIV drug resistance in resource-limited settings.

M. Sc. University of KwaZulu-Natal, Durban 2014.South Africa has one of the highest HIV infection rates in the world with more than 5.6 million infected people and consequently has the largest antiretroviral treatment program with more than 1.5 million people on treatment. The development of drug resistance is a major factor impeding the efficacy of antiretroviral treatment. While genotype resistance testing (GRT) is the standard method to determine resistance, access to these tests is limited in resource-limited settings. This research investigates the efficacy of multi-label machine learning techniques at predicting HIV drug resistance from routine treatment and laboratory data. Six techniques, namely, binary relevance, HOMER, MLkNN, predictive clustering trees (PCT), RAkEL and ensemble of classifier chains (ECC) have been tested and evaluated on data from medical records of patients enrolled in an HIV treatment failure clinic in rural KwaZulu-Natal in South Africa. The performance is measured using five scalar evaluation measures and receiver operating characteristic (ROC) curves. The techniques were found to provide useful predictive information in most cases. The PCT and ECC techniques perform best and have true positive prediction rates of 97% and 98% respectively for specific drugs. The ECC method also achieved an AUC value of 0:83, which is comparable to the current state of the art. All models have been validated using 10 fold cross validation and show increased performance when additional data is added. In order to make use of these techniques in the field, a tool is presented that may, with small modifications, be integrated into public HIV treatment programs in South Africa and could assist clinicians to identify patients with a high probability of drug resistance

Conceptual modeling of public services: A metaphor-oriented analysis

Gegenstand dieser Dissertation ist die konzeptuelle Modellierung öffentlicher Dienstleistungen. Konzeptuelle Modelle öffentlicher Dienstleistungen dienen zur Beschreibung der Anwendungsdomänen von Informationssystemen, die zur Erbringung der öffentlichen Dienstleistungen eingesetzt werden. Sie beschreiben die fachlichen Konzepte, die in der Domäne relevant sind und in den Informationssystemen implementiert werden. Deshalb nehmen die konzeptuellen Modelle im Entwicklungsprozess dieser Informationssysteme eine wichtige Rolle ein. Als sprachliche Ausdrücke spiegeln konzeptuelle Modelle zudem wider, wie der Modellierer die Anwendungsdomäne bewusst aber auch unbewusst konzeptuell erschließt. Vor diesem Hintergrund wird im Rahmen dieser Dissertation die konzeptuelle Modellierung öffentlicher Dienstleistungen aus Sicht der sogenannten Theorie der konzeptuellen Metapher (conceptual metaphor theory) untersucht. Diese Theorie basiert auf der Grundannahme, dass es sich bei Metaphern nicht um Kombinationen von Wörtern aus unterschiedlichen Domänen handelt. Vielmehr sind (konzeptuelle) Metaphern als eine Abbildung von Konzepten einer Quelldomäne auf die Konzepte einer Zieldomäne zu verstehen. Konzeptuelle Metaphern sind folglich ein kognitiver Mechanismus, mittels dessen eine Zieldomäne, wie z. B. die der hier betrachteten Domäne der öffentlichen Dienstleistungen, konzeptuell erschlossen wird. Ziel dieser Dissertation ist es, zu erarbeiten, mit welchen konzeptuellen Metaphern das Konzept der öffentlichen Dienstleistung konzeptuell erschlossen wird. Darüber hinaus soll gezeigt werden, wie die konzeptuellen Metaphern in konzeptuellen Modellen öffentlicher Dienstleistungen realisiert sind. Dazu wird in dieser Dissertation eine für diese Untersuchung geeignete Methode zur Identifikation und Beschreibung von konzeptuellen Metaphern für das Konzept der öffentlichen Dienstleistung erarbeitet. Die Methode wird auf Veröffentlichungen zum Thema der konzeptuellen Modellierung öffentlicher Dienstleistungen angewendet. Das Ergebnis der Untersuchung ist ein Katalog konzeptueller Metaphern für das Konzept der öffentlichen Dienstleistung. Der Katalog umfasst 23 konzeptuelle Metaphern, die modelliert und erläutert werden. Ferner werden Voraussetzungen und Beispiele für die Anwendung der jeweiligen konzeptuellen Metapher gegeben.Subject of this thesis is the conceptual modeling of public services. Conceptual models describe the domains of information systems used for providing public services. Conceptual models of public services play a key role in the development process of such information systems because conceptual models describe the relevant concepts of the domain which will be “implemented” in these information systems. As conceptual models are also lingual artifacts they reflect the modeler’s view on the domain described. This also includes how the modeler himself conceptualizes and understands the domain. Against this background, the conceptual modeling of public services is analyzed from the perspective of the so-called conceptual metaphor theory. The main assumption of the conceptual metaphor theory is that metaphors are not just unusual combination of words coming from different domains but mappings of concepts of a source domain to the concepts of a target domain. As such, conceptual metaphors are a crucial mechanism in understanding and conceptualizing the target domain, such as the domain of public services. This thesis intends to identify conceptual metaphors used to conceptualize the concept of public service and to analyze how such conceptual metaphors are realized in conceptual models for public services. To this end, a definition of the concept of conceptual metaphor as well as a method for identifying, describing, and modeling conceptual metaphors for the concept of public service are developed. Applying the definition and the method on publications regarding the conceptual modeling of public services, 23 conceptual metaphors for the concept of public services are identified, described and modeled. Furthermore, prerequisites and examples for applying these conceptual metaphors in the context of conceptual modeling are given